Movement Classification in Video Using Kinematics-Driven Change Detection and Local Kinematics Shape Pattern
Jing Tian, Li Chen, Xiaoming Liu · 2018
This paper studies the automatic classification of abnormal mutant and normal fishes by analyzing their movements recorded in the video. Motivated by the observation that mutant fishes have muscle disorders so that their bodies cannot bend sufficiently to swim normally, a kinematics-driven movement change detection approach is proposed to automatically segment the recorded video into different video segments. Furthermore, a new feature extraction method, called local kinematics shape pattern (LKSP), is proposed in this paper to provide discriminative spatiotemporal kinematics measurements of fish body movements. The histogram of the proposed LKSP features is incorporated into a motion classification approach to identify whether the fish is normal or a mutant. The experiments are conducted using the real-world recorded videos to demonstrate the superior performance of the proposed approach.